ArticleScience advances2022
Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: The rate-limiting step number.
Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
11 citing papers in PubMed, 23 citations in OpenAlex.
- A history-dependent approach for accurate initial condition estimation in epidemic models.PLoS computational biology · 2025Article
- Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways.International journal of molecular sciences · 2024Review
- Solving stochastic gene-expression models using queueing theory: A tutorial review.Biophysical journal · 2024Review
- Cellular heterogeneity in TNF/TNFR1 signalling: live cell imaging of cell fate decisions in single cells.Cell death & disease · 2024Review
- Density physics-informed neural networks reveal sources of cell heterogeneity in signal transduction.Patterns (New York, N.Y.) · 2024Article
- Quantifying and correcting bias in transcriptional parameter inference from single-cell data.Biophysical journal · 2024Article
- Inferring delays in partially observed gene regulation processes.Bioinformatics (Oxford, England) · 2023Article
- Stochastic modeling of the mRNA life process: A generalized master equation.Biophysical journal · 2023Article
- Distribution modeling quantifies collective TScience advances · 2023Article
- Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes.ACS synthetic biology · 2023Article
- Spatially coordinated collective phosphorylation filters spatiotemporal noises for precise circadian timekeeping.iScience · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying the sources of cell-to-cell variability in signaling dynamics is essential to understand drug response variability and develop effective therapeutics. However, it is challenging because not all signaling intermediate reactions can be experimentally measured simultaneously. This can be overcome by replacing them with a single random time delay, but the resulting process is non-Markovian, making it difficult to infer cell-to-cell heterogeneity in reaction rates and time delays. To address this, we developed an efficient and scalable moment-based Bayesian inference method (MBI) with a user-friendly computational package that infers cell-to-cell heterogeneity in the non-Markovian signaling process. We applied MBI to single-cell expression profiles from promoters responding to antibiotics and discovered a major source of cell-to-cell variability in antibiotic stress response: the number of rate-limiting steps in signaling cascades. This knowledge can help identify effective therapies that destroy all pathogenic or cancer cells, and the approach can be applied to precision medicine.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.